{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Effective Marginal Tax Rates\n",
    "\n",
    " This notebook produces Figure 1 of the paper. It uses SimTax, a tax simulator as reported in Boisclair et al. (2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tax import simtax\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "person = simtax.person\n",
    "hhold = simtax.hhold\n",
    "tax = simtax.tax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "reprate = 0.5\n",
    "earn = np.linspace(20e3,200e3,1000)\n",
    "retinc = [e*reprate for e in earn]\n",
    "mtr_work = []\n",
    "mtr_ret = []\n",
    "\n",
    "for e in earn:\n",
    "    work = hhold(person(age=40,earn = e))\n",
    "    ret = hhold(person(age=65,earn = reprate*e))\n",
    "    this = tax()\n",
    "    mtr_work.append(this.pmtr(work,0,1e3))\n",
    "    mtr_ret.append(this.pmtr(ret,0,1e3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "plt.figure()\n",
    "plt.plot(earn,mtr_work,label='working')\n",
    "plt.plot(earn,mtr_ret,label='retired')\n",
    "plt.xlabel('earnings')\n",
    "plt.ylabel('marginal tax rate')\n",
    "plt.legend()\n",
    "plt.savefig('mtrs.png',dpi=1200)"
   ]
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